Hierarchical Router Dynamic Metadata Aggregation
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Solution Overview
Problem
Current cloud computing models for real-time action and close control loops in data networks face challenges due to the high volume of data generated by sensors, which overwhelms access networks and requires static schema modifications, leading to increased latency, cost, and inefficiency in processing and storage.
Innovation Solution
Implementing a hierarchical data collection system where routers receive and convert data from lower devices into aggregated metadata using dynamic schemas, allowing for efficient storage and transmission, and enabling dynamic schema updates based on new data types and queries, thereby reducing redundancy and improving query processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is transmitted to cloud data centers for processing, then centralized data storage and processing is achieved, but network bandwidth is overwhelmed and latency increases
Solution Approach 1:
The patent segments the centralized cloud processing architecture into distributed hierarchical processing units located at network edges (access routers). Data processing is divided across multiple levels: edge routers perform local aggregation and filtering, regional servers handle intermediate processing, and cloud data centers manage overall coordination. This segmentation reduces network bandwidth consumption by processing data locally rather than transmitting all raw data to centralized cloud data centers.
Solution Approach 2:
The patent introduces a hierarchical spatial dimension to data processing architecture, organizing processing resources across multiple levels (edge routers, regional servers, cloud data centers) and geographic locations. This dimensional change allows data to be processed at the closest appropriate level in the hierarchy, reducing the need to transmit all data across the entire network to centralized cloud data centers, thereby conserving network bandwidth while maintaining processing reliability.
2Stability of the object's composition
If static schema is used for data storage, then data structure consistency is maintained, but schema modification and re-indexing become difficult when sensor types change
Solution Approach 1:
The patent implements dynamic schemas that can automatically adapt to new sensor types and data formats. The schema evolution mechanism allows the data structure to change over time based on incoming data characteristics, enabling the system to accommodate new sensor types without manual reconfiguration. This dynamic approach maintains data consistency through structured evolution while providing adaptability to changing sensor landscapes.
Solution Approach 2:
The patent employs parameter change mechanisms where schema parameters (data types, formats, structures) are automatically adjusted based on incoming sensor data characteristics. When new sensor types are detected, the system modifies schema parameters to accommodate the new data formats while maintaining backward compatibility with existing data structures. This enables seamless adaptation to new sensor types without disrupting overall data consistency.
3Loss of time
If data is aggregated and processed at edge routers, then network bandwidth is reduced and latency is lowered, but device complexity increases
Solution Approach 1:
The patent segments processing functions across the hierarchical architecture, placing only aggregation and filtering functions at edge routers, while more complex processing tasks are distributed to regional servers and cloud data centers. This segmentation reduces edge router complexity by limiting their functional responsibilities to basic data preparation tasks, while still achieving low latency for time-critical operations through local processing.
Solution Approach 2:
The patent implements multi-functional edge routers that can perform multiple roles: data aggregation, filtering, local querying, and forwarding. By designing edge routers with universal capabilities to handle various data processing tasks, the system reduces the need for specialized complex devices at each level, achieving efficient local processing without proportionally increasing device complexity.
Data Source
AI summary
In one embodiment, a router operating in a hierarchically routed computer network may receive collected data from one or more hierarchically lower devices in the network (e.g., hierarchically lower sensors or routers). The collected data may then be converted to aggregated metadata according to a dynamic schema, and the aggregated metadata is stored at the router. The aggregated metadata may also be transmitted to one or more hierarchically higher routers in the network. Queries may then be served by the router based on the aggregated metadata, accordingly.